Driver fatigue and lack of sleep of drivers especially those that drive large vehicles such as trucks, buses, etc. is a long standing problem. Each year numerous road accidents and fatalities occur as a result of sleepy individuals falling asleep while driving. If at all we can detect the drowsiness state of the driver and have mechanism to warn the driver in such a state, such accidents may be prevented to a large extent.
Absence of a method for detection of driver drowsiness in automobiles is long standing problem.
Some of the inventions which deals with eye tracking and driver drowsiness identification known to us are as follows:
U.S. Pat. No. 6,283,954 to Kingman Yee teaches improved devices, systems, and methods for sensing and tracking the position of an eye make use of the contrast between the sclera and iris to derive eye position. This invention is particularly useful for tracking the position of the eye during laser eye surgery, such as photorefractive keratectomy (PRK), phototherapeutic keratectomy (PTK), laser in situ keratomileusis (LASIK), or the like.
U.S. Pat. No. 5,345,281 to Taboada et al discloses about devices for tracking the gaze of the human eye, and more particularly to an optical device for tracking eye movement by analysis of the reflection off the eye of an infrared (IR) beam.
U.S. Pat. No. 6,927,694 to Smith et al discloses tracking a person's head and facial features with a single on-board camera with a fully automatic system that can initialize automatically, and can reinitialize when needed and provides outputs in realtime. The system as proposed in '694 uses RGB array indexing on the R, G, B components for the pixel that is marked as important, the system also works on different algorithms for daytime and nighttime conditions.
U.S. Pat. No. 5,689,241 to Clarke Sr et al discloses about the device that monitors via the infrared camera the thermal image changes in pixel color of open versus closed eyes of the driver via the temperature sensitive infrared portion of the digitized photographic image passed through a video charge coupling device. The combination of non movement and a decrease in breath temperature, which is a physiological response to hypoventilation thus initiating drowsiness, will trigger the infrared camera to zoom onto the eye region of the driver.
U.S. Pat. No. 5,900,819 to Christos T. Kyrtso discloses about detect drowsiness by measuring vehicle behaviors including speed, lateral position, turning angle.
US20080252745 to Tomokazu Nakamura teaches state-of-eye (including blinking) distinguishing means by calculating a feature value that represents a state of an eye, for an eye-area based on pixel data of pixels that constitute the eye-area. A threshold value setting means calculates a first threshold value representing a feature value at a first transition point from an open state to a closed state and a second threshold value representing a feature value at a second transition point from the closed state to the open state, based on a feature value calculated for a targeted eye when the targeted eye is open.
U.S. Pat. No. 7,362,885 to Riad I. Hammoud teaches that an object tracking method tracks a target object between successively generated infrared video images using a grey-scale hat filter to extract the target object from the background. Where the object is a person's eye, the eye state and decision confidence are determined by analyzing the shape and appearance of the binary blob along with changes in its size and the previous eye state, and applying corresponding parameters to an eye state decision matrix.
U.S. Pat. No. 7,130,446 to Rui et al teaches that automatic detection and tracking of multiple individuals includes receiving a frame of video and/or audio content and identifying a candidate area for a new face region in the frame. One or more hierarchical verification levels are used to verify whether a human face is in the candidate area, and an indication made that the candidate area includes a face if the one or more hierarchical verification levels verify that a human face is in the candidate area. A plurality of audio and/or video cues are used to track each verified face in the video content from frame to frame.
Most of these known drowsiness detection devices rely on sensor technologies. Although some of the approaches have been made using computer vision technology, these drowsiness detection devices use complex methods in order to detect drowsiness and are costlier. Overall these methods aren't adequate and accurate to track eye region to monitor alertness of drivers who suffers from fatigue and lack of sleep.
Thus, in the light of the above mentioned background of the art, it is evident that, there is a need for a system and method for eye tracking and driver drowsiness identification, which is simple, easy to install and provides higher accuracy at a lower cost.